And just then I realized the problem. nknots need to be length(knots).
Otherwise knots are deleted.
I am not so sure this works equally well as my original loess fit
though. The fit I get with cobs is highly dependent on the "knot step
size". At 0.4 for example it seems ok. At 0.3 I get points
Sorry. I have updated the code to have include the knot selection
(https://github.com/stanstrup/retpred_shiny/blob/master/retdb_admin/make_predictions_CI_tests.R).
I am working on the "Good data" at the moment.
- Jan.
On 08/18/2014 08:14 PM, David Winsemius wrote:
> I had that result somet
I had that result sometimes when testing as well. You don't offer any code so
there's nothing I can do to follow-up.
--
David.
On Aug 18, 2014, at 4:56 AM, Jan Stanstrup wrote:
> The knots are deleted anyway ("Deleting unnecessary knots ..."). It seems to
> make no difference.
>
>
>
> On 08
The knots are deleted anyway ("Deleting unnecessary knots ..."). It
seems to make no difference.
On 08/14/2014 06:06 PM, David Winsemius wrote:
On Aug 14, 2014, at 7:17 AM, Jan Stanstrup wrote:
Thank you very much for this snippet!
I used it on my data and indeed it does give intervals wh
On Aug 14, 2014, at 9:06 AM, David Winsemius wrote:
>
> On Aug 14, 2014, at 7:17 AM, Jan Stanstrup wrote:
>
>> Thank you very much for this snippet!
>>
>> I used it on my data and indeed it does give intervals which appear quite
>> realistic (script and data here
>> https://github.com/stanst
On Aug 14, 2014, at 7:17 AM, Jan Stanstrup wrote:
> Thank you very much for this snippet!
>
> I used it on my data and indeed it does give intervals which appear quite
> realistic (script and data here
> https://github.com/stanstrup/retpred_shiny/blob/master/retdb_admin/make_predictions_CI_tes
Thank you very much for this snippet!
I used it on my data and indeed it does give intervals which appear
quite realistic (script and data here
https://github.com/stanstrup/retpred_shiny/blob/master/retdb_admin/make_predictions_CI_tests.R).
I also tried getting the intervals with predict.cobs b
On Aug 12, 2014, at 8:40 AM, Bert Gunter wrote:
> PI's of what? -- future individual values or mean values?
>
> I assume quantreg provides quantiles for the latter, not the former.
> (See ?predict.lm for a terse explanation of the difference).
I probably should have questioned the poster about
To follow up on David's suggestion on this thread, I might add that the
demo(predemo)
in my quantreg package illustrates a variety of approaches to prediction
intervals for
quantile regression estimates. Adapting this to monotone nonparametric
estimation
using rqss() or cobs would be quite st
Thanks to all of you for your suggestions and comments. I really
appreciate it.
Some comments to Dennis' comments:
1) I am not concerned about predicting outside the original range. That
would be nonsense anyway considering the physical phenomenon I am
modeling. I am, however, concerned that t
PI's of what? -- future individual values or mean values?
I assume quantreg provides quantiles for the latter, not the former.
(See ?predict.lm for a terse explanation of the difference). Both are
obtainable from bootstrapping but the details depend on what you are
prepared to assume. Consult refe
On Aug 12, 2014, at 12:23 AM, Jan Stanstrup wrote:
> Hi,
>
> I am trying to find a way to estimate prediction intervals (PI) for a
> monotonic loess curve using bootstrapping.
>
> At the moment my approach is to use the boot function from the boot package
> to bootstrap my loess model, which
Hi,
I am trying to find a way to estimate prediction intervals (PI) for a
monotonic loess curve using bootstrapping.
At the moment my approach is to use the boot function from the boot
package to bootstrap my loess model, which consist of loess + monoproc
from the monoproc package (to force
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